18555501. CAPABILITY INDICATION FOR A MULTI-BLOCK MACHINE LEARNING MODEL simplified abstract (QUALCOMM Incorporated)
Contents
- 1 CAPABILITY INDICATION FOR A MULTI-BLOCK MACHINE LEARNING MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CAPABILITY INDICATION FOR A MULTI-BLOCK MACHINE LEARNING MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Wireless Communications and Machine Learning
- 1.13 Original Abstract Submitted
CAPABILITY INDICATION FOR A MULTI-BLOCK MACHINE LEARNING MODEL
Organization Name
Inventor(s)
CAPABILITY INDICATION FOR A MULTI-BLOCK MACHINE LEARNING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18555501 titled 'CAPABILITY INDICATION FOR A MULTI-BLOCK MACHINE LEARNING MODEL
Simplified Explanation
The patent application describes methods, systems, and devices for wireless communications that involve user equipment indicating support for a multi-block machine learning application. This includes capabilities for backbone blocks and task-specific blocks within the application.
- User equipment can indicate support for different blocks of a machine learning application.
- The user equipment may transmit separate indications for different capabilities within the application.
- Base stations can determine specific capabilities based on a general machine learning capability indication.
Key Features and Innovation
- User equipment can indicate support for specific blocks within a multi-block machine learning application.
- Separate indications can be transmitted for backbone blocks and task-specific blocks.
- Base stations can determine capabilities based on a general machine learning capability indication.
Potential Applications
This technology can be applied in various wireless communication systems where machine learning applications are utilized. It can enhance the efficiency and performance of such systems by allowing user equipment to indicate support for specific blocks within the application.
Problems Solved
This technology addresses the need for user equipment to communicate its capabilities for different blocks within a machine learning application. It streamlines the process of determining specific capabilities and improves the overall functionality of wireless communication systems.
Benefits
- Improved efficiency in wireless communication systems.
- Enhanced performance of machine learning applications.
- Streamlined process for determining user equipment capabilities.
Commercial Applications
- This technology can be used in telecommunications networks to optimize machine learning applications.
- It can be implemented in IoT devices to enhance data processing capabilities.
- Companies developing wireless communication systems can benefit from the improved efficiency and performance offered by this technology.
Prior Art
For information on prior art related to this technology, researchers can explore patents and publications in the field of wireless communications and machine learning applications.
Frequently Updated Research
Researchers interested in this technology can stay updated on advancements in wireless communication systems and machine learning applications through academic journals, conferences, and industry reports.
Questions about Wireless Communications and Machine Learning
How does this technology improve the efficiency of wireless communication systems?
This technology allows user equipment to indicate support for specific blocks within a machine learning application, enabling more streamlined and optimized processes.
What are the potential commercial applications of this technology?
This technology can be applied in various industries, including telecommunications, IoT, and wireless communication system development, to enhance efficiency and performance.
Original Abstract Submitted
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may indicate a support for an end-to-end multi-block machine learning application, a first UE capability for a backbone block of the multi-block machine learning application that makes up one or more front-end layers (e.g., one or more backbone layers), a second UE capability for a task-specific block of the multi-block machine learning application that makes up the end layer (s) of the end-to-end model (e.g., one or more task-specific layers), or a combination thereof. In some examples, the UE may transmit separate indications for the first UE capability and for the second UE capability. Additionally or alternatively, the UE may transmit a general machine learning capability indication, where a base station then determines the first UE capability for the base stage and the second UE capability for the task-specific stage from the general machine learning capability.
